2-D inversion of gravity data including depth weighting and compactness constraints: a case study on the data set of the manganese mine of Safo
عنوان دوره: نوزدهمین کنفرانس ژئوفیزیک ایران
کد مقاله : 1476-NIGS
نویسندگان
1موسسه ژئوفیزیک
2موسسه ژئوفیزیک، دانشگاه تهران
چکیده
Gravity surveys have been used for a wide range of studies such as oil and gas exploration, mining applications and mapping bedrock topography. Due to the existence of 2-D geologic structures such as fracture zones, faults, dikes, rift zones and anticlines, 2-D inversion of gravity data is very practical. Gravity data inversion has two main problems about non-uniqueness and instability of the solution which can be obviated by using constraints and a priori information. In this paper, an inversion algorithm based on inserting compactness and depth weighting constraints in the weighted minimum length solution is introduced and truncated singular value decomposition (TSVD) is manipulated as regularization. At first the inversion algorithm is applied on the synthetic data for free noisy and high level noisy cases. Ultimately, the productivity of the algorithm is tested by applying it on the real data of the manganese mine of Safo.
کلیدواژه ها
Title
2-D inversion of gravity data including depth weighting and compactness constraints: a case study on the data set of the manganese mine of Safo
Authors
Ramin Varfinezhad, Vahid Ebrahimzadeh Ardestani
Abstract
Gravity surveys have been used for a wide range of studies such as oil and gas exploration, mining applications and mapping bedrock topography. Due to the existence of 2-D geologic structures such as fracture zones, faults, dikes, rift zones and anticlines, 2-D inversion of gravity data is very practical. Gravity data inversion has two main problems about non-uniqueness and instability of the solution which can be obviated by using constraints and a priori information. In this paper, an inversion algorithm based on inserting compactness and depth weighting constraints in the weighted minimum length solution is introduced and truncated singular value decomposition (TSVD) is manipulated as regularization. At first the inversion algorithm is applied on the synthetic data for free noisy and high level noisy cases. Ultimately, the productivity of the algorithm is tested by applying it on the real data of the manganese mine of Safo.
Keywords
Gravity, Inversion, Real data, Synthetic data